{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 6  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_research_assistants'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_121636-ntp9oned</code>"
      ],
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned' target=\"_blank\">finetune_chetGPT_hatespeech_research_assistants</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x1fb5ada77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_6__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_6__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_6__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_6__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.8299492385787, 869.0\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.233502538071066, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346653 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733265 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 879.15, 868.0\n",
      "p5 / p95: 853.9, 918.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.91, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87915 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439575 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434568\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-Gq5U3NidVOT9juR0IwYANHTM\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-6-t-1-trn-100:B5t1XX0N has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.09, validation loss=0.11, full validation loss=0.10\n",
      "Step 11/25: training loss=0.05, validation loss=0.06\n",
      "Step 12/25: training loss=0.08, validation loss=0.09\n",
      "Step 13/25: training loss=0.07, validation loss=0.11\n",
      "Step 14/25: training loss=0.07, validation loss=0.15\n",
      "Step 15/25: training loss=0.09, validation loss=0.17, full validation loss=0.12\n",
      "Step 16/25: training loss=0.05, validation loss=0.15\n",
      "Step 17/25: training loss=0.06, validation loss=0.12\n",
      "Step 18/25: training loss=0.02, validation loss=0.08\n",
      "Step 19/25: training loss=0.04, validation loss=0.17\n",
      "Step 20/25: training loss=0.02, validation loss=0.12, full validation loss=0.14\n",
      "Step 21/25: training loss=0.03, validation loss=0.30\n",
      "Step 22/25: training loss=0.04, validation loss=0.10\n",
      "Step 23/25: training loss=0.03, validation loss=0.22\n",
      "Step 24/25: training loss=0.03, validation loss=0.08\n",
      "Step 25/25: training loss=0.05, validation loss=0.07, full validation loss=0.14\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:43<00:00,  1.77it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6545454545454545,\n",
      "                 'precision': 0.6474820143884892,\n",
      "                 'recall': 0.6617647058823529,\n",
      "                 'support': 136},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8043478260869564,\n",
      "                       'precision': 0.7974137931034483,\n",
      "                       'recall': 0.8114035087719298,\n",
      "                       'support': 228},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.5660377358490566,\n",
      "                  'precision': 0.6521739130434783,\n",
      "                  'recall': 0.5,\n",
      "                  'support': 30},\n",
      " 'accuracy': 0.7360406091370558,\n",
      " 'macro avg': {'f1-score': 0.6749770054938224,\n",
      "               'precision': 0.6990232401784718,\n",
      "               'recall': 0.6577227382180942,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.734493954927613,\n",
      "                  'precision': 0.7346018177048861,\n",
      "                  'recall': 0.7360406091370558,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DP5B6vwpSVQq81/AkcotssC2lYj9nMq/cPFud+/Xl5dtA9UCmKd+wKxADuv+Jf/51xo0MBQb0OI28Amv8eqSWJUImM9q0VIk56y/grVE3cWCHC+oGP0C3frfw5QQ/S4f2QjHHsC2HfUlLYWEhVq5ciSlTpqBhw4Za+4YOHYqZM2fi9OnTCA4ONqi9zMxM5OXlaZW5urrC1tZWp25sbCwePnyIbt26wdfXF1lZWZg/fz4KCwvRsWNHo97H/fv3de4naG9vr3XbmTt37ujUqVKlCuRyOaKiorBjxw60b98eU6dOxcsvvwwnJyckJydjxowZSEhI4K1ejOBkm4+YJr9Daf8AWQVy7LpeE3NOvYQi4dEw+7fnG0NmU4TYlw4+usnzbQ+88+uryCmSPqVlqmzW7giCTFqEDwcfgZN9AS5cc8fYmV2Qm6f7O4Eql0t/OmLKyNp4Z8wNDBj9DzLSZPhmag3s2+Jm6dDoBcLkrwJs3boVd+7cwWuvvaazr06dOggKCkJCQgLmz59vUHv6FngcOXIELVvqDue1bdsWX3/9NQYNGoR///0XVatWRZMmTfDLL7+UuVBEn0mTJmHSpElaZSNGjMA333yjed2hQwed49asWYO+fftCJpNh9+7dmDt3LhYvXoyYmBjY29ujfv36GD16tE5iTGX7+Xpt/Hy9rF48CRb8GYIFf4Y8s5jo2fg4vtsTJRIs39QUyzc1tUg8VLF+31sVv+/l01oqUjFMH7YtNrL+gQMH8MUXX+DEiRNIT0/Hpk2btG7DFhERgeXLl2sd06JFC60HJ+Tn5yMmJgZr1qxBbm4u2rdvj4ULF6J69epGxSIRBK4PoxeDSqWCQqGA39TpsJLLLR0OVTCfX3ifSDGRHjTu9lVUORUJhdiX/wOys7N1Hm5gDiV/J/5ztBPkjqb1lOc9KMS0lr8YHOvPP/+M3377DU2bNkXv3r31Jn///vuv1gMfpFIpXFz+94jO9957D9u2bUNiYiJcXV0RHR2Nu3fv4sSJE5rFloZgzx8RERGJSrFghWIT5+wZe3zXrl2fegszmUxW6oMTsrOzkZCQgJUrV2pG3b7//nv4+Phgz5496Ny5s8GxVN7ZikREREQW9uTDBsq6r+7TJCUlwcPDAwEBARg2bBgyMzM1+06cOIHCwkJ06tRJU+bt7Y2GDRvi8OHDRp2HyR8RERGJigAJ1CZuJTfQ9/Hx0XrgQHx8fLli6tq1K1atWoW9e/di9uzZOH78ONq1a6dJJjMyMiCVSlG1qvZ8UE9PT52Fl0/DYV8iIiISFXMO+6alpWnN+Svv/WffeustzdcNGzZE8+bN4evrix07duD1118v9ThjHqhQgj1/REREROXk7OystZnr4QNeXl7w9fXF5cuXAQBKpRIFBQW4d++eVr3MzEx4enoa1TaTPyIiIhIVtSAxy1aR7ty5g7S0NHh5eQEAmjVrBltbW+zevVtTJz09HWfPnkXr1q2NapvDvkRERCQqxbBCsYn9X8Ye/+DBA1y5ckXzOiUlBadOnYKLiwtcXFwQGxuL3r17w8vLC6mpqZgwYQLc3Nw09wxWKBQYMmQIoqOj4erqChcXF8TExCAoKEjvPXfLwuSPiIiIqIIlJycjPDxc8/rjjz8GAAwePBiLFi3CmTNnsGLFCmRlZcHLywvh4eFYt24dnJycNMfMnTsXNjY2ePPNNzU3eU5MTDTqHn8Akz8iIiISGXMM2xp7fFhYGMp6rsauXbue2oZcLseCBQuwYMECo879JCZ/REREJCpqWEFt4rCvqcdbUuWNnIiIiIiMxp4/IiIiEpViQYJiE4d9TT3ekpj8ERERkahYYs7f84TJHxEREYmKIFhBbeITPgQTj7ekyhs5ERERERmNPX9EREQkKsWQoBgmzvkz8XhLYvJHREREoqIWTJ+zpy79ln3PPQ77EhEREYkIe/6IiIhIVNRmWPBh6vGWxOSPiIiIREUNCdQmztkz9XhLqrxpKxEREREZjT1/REREJCp8wgcRERGRiIh9zl/ljZyIiIiIjMaePyIiIhIVNczwbN9KvOCDyR8RERGJimCG1b4Ckz8iIiKiykEtmKHnrxIv+OCcPyIiIiIRYc8fERERiYrYV/sy+SMiIiJR4bAvEREREYkGe/6IiIhIVMT+bF8mf0RERCQqHPYlIiIiItFgzx8RERGJith7/pj8ERERkaiIPfnjsC8RERGRiLDnj4iIiERF7D1/TP6IiIhIVASYfqsWwTyhWASTPyIiIhIVsff8cc4fERERkYiw54+IiIhERew9f0z+iIiISFTEnvxx2JeIiIhIRNjzR0RERKIi9p4/Jn9EREQkKoIggWBi8mbq8ZbEYV8iIiIiEWHPHxEREYmKGhKTb/Js6vGWxOSPiIiIREXsc/447EtERERUwQ4cOIDu3bvD29sbEokEmzdv1uwrLCzEuHHjEBQUBAcHB3h7e2PQoEG4efOmVhthYWGQSCRaW9++fY2OhckfERERiUrJgg9TN2Pk5OQgODgYX331lc6+hw8f4uTJk/j0009x8uRJbNy4EZcuXUKPHj106g4bNgzp6emabfHixUa/fw77EhERkaiYc9hXpVJplctkMshkMp36Xbt2RdeuXfW2pVAosHv3bq2yBQsW4KWXXsL169dRo0YNTbm9vT2USqVJsbPnj4iIiETFnD1/Pj4+UCgUmi0+Pt4sMWZnZ0MikaBKlSpa5atWrYKbmxsCAwMRExOD+/fvG902e/6IiIiIyiktLQ3Ozs6a1/p6/YyVl5eHTz75BP3799dqe8CAAahZsyaUSiXOnj2L8ePH4/Tp0zq9hk/D5I9eONWSCmFjY23pMKiC7V2RYOkQ6Bnq7N3Y0iHQMyAIhc/oPKYP+5b0/Dk7O2slaKYqLCxE3759oVarsXDhQq19w4YN03zdsGFD1KlTB82bN8fJkyfRtGlTg8/BYV8iIiISFQGAIJi4VUBchYWFePPNN5GSkoLdu3c/Nals2rQpbG1tcfnyZaPOw54/IiIiIgsrSfwuX76Mffv2wdXV9anHnDt3DoWFhfDy8jLqXEz+iIiISFTUkEDyjJ/w8eDBA1y5ckXzOiUlBadOnYKLiwu8vb3xxhtv4OTJk9i+fTuKi4uRkZEBAHBxcYFUKsXVq1exatUqdOvWDW5ubjh//jyio6PRpEkThIaGGhULkz8iIiISlfLcp09fG8ZITk5GeHi45vXHH38MABg8eDBiY2OxdetWAEDjxo21jtu3bx/CwsIglUrx66+/4ssvv8SDBw/g4+ODV155BZMnT4a1tXHz3Jn8EREREVWwsLAwCELpMwXL2gc8uqXM/v37zRILkz8iIiISFbUggUTEz/Zl8kdERESiUrJi19Q2Kive6oWIiIhIRNjzR0RERKJiiQUfzxMmf0RERCQqTP6IiIiIRETsCz4454+IiIhIRNjzR0RERKIi9tW+TP6IiIhIVB4lf6bO+TNTMBbAYV8iIiIiEWHPHxEREYkKV/sSERERiYjw383UNiorDvsSERERiQh7/oiIiEhUOOxLREREJCYiH/dl8kdERETiYoaeP1Tinj/O+SMiIiISEfb8ERERkajwCR9EREREIiL2BR8c9iUiIiISEfb8ERERkbgIEtMXbFTinj8mf0RERCQqYp/zx2FfIiIiIhFhzx8RERGJC2/yTERERCQeYl/ta1DyN3/+fIMbHD16dLmDISIiIqKKZVDyN3fuXIMak0gkTP6IiIjo+VeJh21NZVDyl5KSUtFxEBERET0TYh/2Lfdq34KCAly8eBFFRUXmjIeIiIioYglm2iopo5O/hw8fYsiQIbC3t0dgYCCuX78O4NFcv88//9zsARIRERGR+Rid/I0fPx6nT59GUlIS5HK5prxDhw5Yt26dWYMjIiIiMj+JmbbKyehbvWzevBnr1q1Dy5YtIZH87403aNAAV69eNWtwRERERGYn8vv8Gd3zd+vWLXh4eOiU5+TkaCWDRERERPT8MTr5CwkJwY4dOzSvSxK+JUuWoFWrVuaLjIiIiKgiiHzBh9HDvvHx8ejSpQvOnz+PoqIifPnllzh37hyOHDmC/fv3V0SMREREROYjSB5tprZRSRnd89e6dWv89ttvePjwIWrVqoVffvkFnp6eOHLkCJo1a1YRMRIRERGRmZTr2b5BQUFYvny5uWMhIiIiqnCC8GgztY3KqlzJX3FxMTZt2oQLFy5AIpGgfv366NmzJ2xsytUcERER0bMj8tW+RmdrZ8+eRc+ePZGRkYG6desCAC5dugR3d3ds3boVQUFBZg+SiIiIiMzD6Dl/Q4cORWBgIG7cuIGTJ0/i5MmTSEtLQ6NGjTB8+PCKiJGIiIjIfEoWfJi6GeHAgQPo3r07vL29IZFIsHnzZu2QBAGxsbHw9vaGnZ0dwsLCcO7cOa06+fn5+OCDD+Dm5gYHBwf06NEDN27cMPrtG538nT59GvHx8ahataqmrGrVqpg+fTpOnTpldABEREREz5JEMM9mjJycHAQHB+Orr77Su3/mzJmYM2cOvvrqKxw/fhxKpRIdO3bE/fv3NXWioqKwadMmrF27FocOHcKDBw/w6quvori42KhYjB72rVu3Lv79918EBgZqlWdmZqJ27drGNkdERET0bFlgzl/Xrl3RtWtX/U0JAubNm4eJEyfi9ddfBwAsX74cnp6eWL16NUaMGIHs7GwkJCRg5cqV6NChAwDg+++/h4+PD/bs2YPOnTsbHItBPX8qlUqzxcXFYfTo0Vi/fj1u3LiBGzduYP369YiKisKMGTMMPjERERFRZfd4jqRSqZCfn290GykpKcjIyECnTp00ZTKZDG3btsXhw4cBACdOnEBhYaFWHW9vbzRs2FBTx1AG9fxVqVJF69FtgiDgzTff1JQJ/13v3L17d6O7HomIiIieKTPe5NnHx0erePLkyYiNjTWqqYyMDACAp6enVrmnpyf+/vtvTR2pVKo17a6kTsnxhjIo+du3b59RjRIRERE9t8w47JuWlgZnZ2dNsUwmK3eTj3e0AY86154s0wnDgDpPMij5a9u2rVGNEhEREYmBs7OzVvJXHkqlEsCj3j0vLy9NeWZmpqY3UKlUoqCgAPfu3dPq/cvMzETr1q2NOp/Rq31LPHz4EH/99Rf+/PNPrY2IiIjouSaYaTOTmjVrQqlUYvfu3ZqygoIC7N+/X5PYNWvWDLa2tlp10tPTcfbsWaOTP6NX+966dQvvvPMOfv75Z737OeePiIiInmsWWO374MEDXLlyRfM6JSUFp06dgouLC2rUqIGoqCjExcWhTp06qFOnDuLi4mBvb4/+/fsDABQKBYYMGYLo6Gi4urrCxcUFMTExCAoK0qz+NZTRyV9UVBTu3buHo0ePIjw8HJs2bcK///6LadOmYfbs2cY2R0RERPTCS05ORnh4uOb1xx9/DAAYPHgwEhMTMXbsWOTm5mLUqFG4d+8eWrRogV9++QVOTk6aY+bOnQsbGxu8+eabyM3NRfv27ZGYmAhra2ujYjE6+du7dy+2bNmCkJAQWFlZwdfXFx07doSzszPi4+PxyiuvGNskERER0bNjxtW+hgoLC9PcHUUfiUSC2NjYMlcKy+VyLFiwAAsWLDDq3E8yes5fTk4OPDw8AAAuLi64desWACAoKAgnT540KRgiIiKiimaJJ3w8T8r1hI+LFy/Cz88PjRs3xuLFi+Hn54dvvvlGa4UKEVWMfq+cxrA+ydjwSyC+Xt0SAPB/zVLxathfCPC7DYVTPoZN6oWr110tHCkZ4sxRB/y40AOXz9jj7r+2mJyQgtZdszX7c3OskDDdC0d2KaC6ZwPP6gXoOeQWug++o6kzpndt/HnEUavdtj3uYcI3fz+z90Hm8db7/yK0WzZ8auejIM8K55PtkTDdCzeuyi0dGr1AjO75i4qKQnp6OoBHNzLcuXMnatSogfnz5yMuLs6otiIiItCrVy+tsvXr10Mul2PmzJkAgNjYWEgkEp2tXr16mmPCwsIQFRWl9VoikWDt2rVabc+bNw9+fn6a14mJiXrblstL/yFLSkqCRCJBVlaWzj4/Pz/MmzdPpzwuLg7W1tb4/PPPterqO3fJFhYWVma9x9t60rVr19CvXz94e3tDLpejevXq6NmzJy5duqSp83hbTk5OaN68OTZu3KjZb+h111dn5MiRWvHs27cP3bp1g6urK+zt7dGgQQNER0fjn3/+Kfc1Fau6NW/h1bC/cPW6i1a5XFaIs5c9seTHEAtFRuWV99AK/oG5iJyu/+Hs30yuhuQkZ4xdcB1L9v+F14ffwsL/VMfhndq3lug64DbWnDqr2T6cmfYswicza9QqB9sS3RD1ah2M7+sPa2sBcWuuQWbHxZRm9Zyt9n3WjO75GzBggObrJk2aIDU1FX/99Rdq1KgBNzc3k4L57rvvEBkZia+//hpDhw7VlAcGBmLPnj1adW1syg5dLpfjP//5D3r37g1bW9tS6zk7O+PixYtaZcbeLPFpli1bhrFjx2Lp0qX45JNPAADHjx/XrIw+fPgwevfujYsXL2ruFSSVSjXHT5kyBcOGDdNq8/EJoI8rKChAx44dUa9ePWzcuBFeXl64ceMGfvrpJ2RnZ2vVXbZsGbp06YKsrCx88cUX6NOnDw4dOoRWrVoBMOy6Dxs2DFOmTNEqs7e313y9ePFijBo1CoMHD8aGDRvg5+eH69evY8WKFZg9ezbmzJlT9sUjDbmsEBNGJGH2spcxsMcprX27D9cBAHi63ddzJD3PQtrdR0i70j+3Cyfs0bHPXQS3fgAA6DbwDnasdMXlP+3RuotKU09mJ8DFo6jC46WKNXGAv9br2R/VwA9nz6FOo1ycPeZYylFExjE6+XuSvb09mjZtanIgM2fOxKRJk7B69Wr07t1ba5+NjY3mBoiG6tevH7Zt24YlS5Zg1KhRpdaTSCRGt22M/fv3Izc3F1OmTMGKFStw4MABtGnTBu7u7po6Li6PenE8PDxQpUoVnTacnJwMjvH8+fO4du0a9u7dC19fXwCAr68vQkNDdepWqVIFSqUSSqUS33zzDdauXYutW7dqkj9Drru9vX2pdW7cuIHRo0dj9OjRmDt3rqbcz88Pbdq00dvTR6X78O3DOHbaByfPV9NJ/ujFFfhSDo7+okDnvnfhqizE6cOO+OeaDM2maCeM+zZWxd4NVVHFvRAh4fcxMDoD9o5qC0VN5uLg/KiT4H6Wcas5qWwSmD5nz7zdRM+WQclfyXJkQ5SnJ+eTTz7B119/je3btxt9r5rSODs7Y8KECZgyZQoGDx4MBwcHs7RrrISEBPTr1w+2trbo168fEhIS0KZNmwo7n7u7O6ysrLB+/XpERUUZvPzb1tYWNjY2KCwsNFssP/74IwoKCjB27Fi9+/UlusbIz8/XeoC2SqUqo3blFt7iKgL8bmPkZz0tHQo9Y6Om/oN5Y3wwoFkgrG0EWFkJiJqVhoYtcjR1wl+/C6VPAVw8ipD6lxxL471w7bwdPl931YKRk+kEDI+9ibPHHPD3RTtLB0MvEIPm/P3xxx8GbadOnTI6gJ9//hkzZszAli1bSk38zpw5A0dHR63t8WHh0owaNQpyubzMhDQ7O1un7U6dOj217erVq+scd/36da06KpUKGzZswMCBAwEAAwcOxPr1641OUsaNG6dzrqSkJL11q1Wrhvnz52PSpEmoWrUq2rVrh6lTp+LatWultp+fn49p06ZBpVKhffv2mnJDrvvChQt16ixfvhwAcPnyZTg7Oxu8EMiQa/q4+Ph4KBQKzfbkw7VfFO4uDxDZ/yimLw5DYaHJnfVUyWxOcMNfJ+zxWeI1fLXzIoZNuomvxlfHyQP/GwLsNuAumrZ5AL96eQjrlYVPl6Tij4NOuPwnE4bKLDLuH9Ssn4v4UTUsHcqLp+RWL6ZulZRBf0n27dtXYQE0atQIt2/fxqRJkxASEqJ3LlvdunWxdetWrbLS5rw9TiaTYcqUKXj//ffx3nvv6a3j5OSkc4saO7un/8I8ePCgTgwlizRKrF69Gv7+/ggODgYANG7cGP7+/li7di2GDx/+1HOUGDNmDCIiIrTKqlWrVmr9yMhIDBo0CPv27cOxY8fw448/Ii4uDlu3bkXHjh019fr16wdra2vk5uZCoVBg1qxZ6Nq1q2a/Idd9wIABmDhxolZZya2AjH3YtCHX9HHjx4/X6pVWqVQvZAIY4HcbLoo8LI7doimzthbQKCADvdqfR+ehEVAL5X5SIz3H8nMlSPzcC5MSUtGiw6N/Gv0b5OHaOTus/8YDTds80Htc7aBc2Niq8U+KDHUa5T7LkMlMRk27gVadVIh+rRZup0uffgAZxwJP+HieWLwboVq1atiwYQPCw8PRpUsX7Ny5UycBkEqlqF27drnaHzhwIGbNmoVp06ZprfQtYWVlVa62a9asqTNs+eRiiKVLl+LcuXNa5Wq1GgkJCUYlf25ubkbH6OTkhB49eqBHjx6YNm0aOnfujGnTpmklf3PnzkWHDh3g7OysSdgeZ8h1VygUpdYJCAhAdnY20tPTDer9M+SaPk4mk0Emkz213cru5HlvvDvxNa2ysUMOIi1DgTU7GjHxe4EVFUlQVGgFKyvtvzJW1gKEMqbz/X1RjqJCK7h6mm8aBz0rAiKn/4PWXbIx5o3a+Dftxf8dR8/ec/FXo0aNGti/fz8yMzPRqVMns87dsrKyQlxcHBYtWoTU1FSztfs0Z86cQXJyMpKSknDq1CnNduDAARw/fhxnz559ZrGU3KIlJydHq1ypVKJ27dp6Ez9zeOONNyCVSjW37XkSF3wYJjdPitR/XLS2vAIbqB7IkfrPo8VCTg75qFXjDvy8swAAPsps1KpxB1UVDy0YORkiN8cKV8/a4erZRyMOGWlSXD1rh8wbtnBwUqNRqwdYMtUbpw87IuO6FL+sc8Ge9S6aewHeTJXi+zmeuHTaDhlpUvz+qxOmDfdD7YYP0SAkp6xT03Po/bh/0O71e/g80he5D6xQ1b0QVd0LIZVz8Y5Z8VYvz4fq1asjKSkJ4eHh6NSpE3bt2gWFQgEAKCoqQkZGhlZ9iUQCT09Pg9p+9dVX0aJFCyxevFjnGEEQdNoGHg1dWlmVPzdOSEjASy+9pHdxR6tWrZCQkKC1ArYs9+/f14nR3t5ec1uYx506dQqTJ0/G22+/jQYNGkAqlWL//v1YunQpxo0bZ9R7MOS6P3z4UKeOTCZD1apV4ePjg7lz5+L999+HSqXCoEGD4Ofnhxs3bmDFihVwdHTk86DNpHWTvzFu6EHN60mjHk3VWL65CZZvNn01PlWcS6ftMfaN//WeL459NKWj45t3ETPvOsYvSsXSOC/MeL8G7mfZwKNaASLGpePVQY9u8mxjK+DUISdsTnBHXo4V3LwL0aK9CgM+zoCRj/uk50D3iEef66yN2ot1ZkX5YPcPLvoOoXIwxxM6RPWEj4pUrVo17N+/H+Hh4ejYsSN++eUXAMC5c+d0hg1lMhny8vIMbnvGjBlo3bq1TrlKpdI7JJmenl7uW8AUFBTg+++/LzXZ6t27N+Lj4zFjxgyt+/mVZtKkSZg0aZJW2YgRI/DNN9/o1K1evTr8/Pzw2WefITU1FRKJRPP6o48+Mup9GHLdlyxZgiVLlmjV6dy5M3bu3Ang0aKbgIAAzJo1C6+99hpyc3Ph5+eHV1991ahV5KTt48+1n6G961AAdh0KsFA0ZIrg1g+w6+apUve7eBQhZl7pN2z2qFaIWRuvVEBkZAmdvYMtHQKJgEQo6ynDRJWISqWCQqFAaPtY2NjwUUgvur2J31k6BHqGOns3tnQI9AwUCYVIwhZkZ2frHd0yVcnfCb9p02FVxtO8DKHOy0PqfyZWWKwVqVzjmitXrkRoaCi8vb3x99+Pnh05b948bNmy5SlHEhEREVmYyOf8GZ38LVq0CB9//DG6deuGrKwszSPKqlSpwmewEhERET3njE7+FixYgCVLlmDixIlaT49o3rw5zpw5Y9bgiIiIiMytZMGHqVtlZfSCj5SUFDRp0kSnXCaT6dxKhIiIiOi5Y44ndFTiJ3wY3fNXs2ZNvY9x+/nnn9GgQQNzxERERERUcUQ+58/onr8xY8YgMjISeXl5EAQBv//+O9asWYP4+Hh89x1X3xERERE9z4xO/t555x0UFRVh7NixePjwIfr3749q1arhyy+/RN++fSsiRiIiIiKz4U2ey2HYsGEYNmwYbt++DbVaXWGPByMiIiIyO3MM24ot+Svh5uZmrjiIiIiI6BkwOvmrWbMmJJLSV7hcu3bNpICIiIiIKpQ5btUipp6/qKgordeFhYX4448/sHPnTowZM8ZccRERERFVDA77GufDDz/UW/71118jOTnZ5ICIiIiIqOKU69m++nTt2hUbNmwwV3NEREREFYP3+TOP9evXw8XFxVzNEREREVUI3urFSE2aNNFa8CEIAjIyMnDr1i0sXLjQrMERERERkXkZnfz16tVL67WVlRXc3d0RFhaGevXqmSsuIiIiIqoARiV/RUVF8PPzQ+fOnaFUKisqJiIiIqKKI/LVvkYt+LCxscF7772H/Pz8ioqHiIiIqEKVzPkzdausjF7t26JFC/zxxx8VEQsRERERVTCj5/yNGjUK0dHRuHHjBpo1awYHBwet/Y0aNTJbcEREREQVohL33JnK4OTv3Xffxbx58/DWW28BAEaPHq3ZJ5FIIAgCJBIJiouLzR8lERERkbmIfM6fwcnf8uXL8fnnnyMlJaUi4yEiIiKiCmRw8icIj1JcX1/fCguGiIiIqKLxJs9GePzmzkRERESVEod9DRcQEPDUBPDu3bsmBUREREREFceo5O+zzz6DQqGoqFiIiIiIKhyHfY3Qt29feHh4VFQsRERERBVP5MO+Bt/kmfP9iIiIiMrHz88PEolEZ4uMjAQARERE6Oxr2bJlhcRi9GpfIiIiokrNAj1/x48f17oX8tmzZ9GxY0f06dNHU9alSxcsW7ZM81oqlZoYpH4GJ39qtbpCAiAiIiJ6lsw550+lUmmVy2QyyGQynfru7u5arz///HPUqlULbdu21TpWqVSaFpgBjH62LxEREVGlJphpA+Dj4wOFQqHZ4uPjn3r6goICfP/993j33Xe1ptUlJSXBw8MDAQEBGDZsGDIzM830hrUZ/WxfIiIiInokLS0Nzs7Omtf6ev2etHnzZmRlZSEiIkJT1rVrV/Tp0we+vr5ISUnBp59+inbt2uHEiRMGtWkMJn9EREQkLmac8+fs7KyV/BkiISEBXbt2hbe3t6bsrbfe0nzdsGFDNG/eHL6+vtixYwdef/11E4PVxuSPiIiIRMWS9/n7+++/sWfPHmzcuLHMel5eXvD19cXly5fLd6IycM4fERER0TOybNkyeHh44JVXXimz3p07d5CWlgYvLy+zx8Dkj4iIiMTFjAs+jKFWq7Fs2TIMHjwYNjb/G3x98OABYmJicOTIEaSmpiIpKQndu3eHm5sbXnvttfK/z1Jw2JeIiIhExVLDvnv27MH169fx7rvvapVbW1vjzJkzWLFiBbKysuDl5YXw8HCsW7cOTk5OpgWqB5M/IiIiomegU6dOeh+aYWdnh127dj2zOJj8ERERkbiI/Nm+TP6IiIhIXESe/HHBBxEREZGIsOePiIiIREXy383UNiorJn9EREQkLiIf9mXyR0RERKJiySd8PA8454+IiIhIRNjzR0REROLCYV8iIiIikanEyZupOOxLREREJCLs+SMiIiJREfuCDyZ/REREJC4in/PHYV8iIiIiEWHPHxEREYkKh32JiIiIxITDvkREREQkFuz5oxeO7OhF2Eiklg6DKlhX/5aWDoGeIRuvKpYOgZ4FdQGQUfGn4bAvERERkZiIfNiXyR8RERGJi8iTP875IyIiIhIR9vwRERGRqHDOHxEREZGYcNiXiIiIiMSCPX9EREQkKhJBgEQwrevO1OMtickfERERiQuHfYmIiIhILNjzR0RERKLC1b5EREREYsJhXyIiIiISC/b8ERERkahw2JeIiIhITEQ+7Mvkj4iIiERF7D1/nPNHREREJCLs+SMiIiJx4bAvERERkbhU5mFbU3HYl4iIiEhE2PNHRERE4iIIjzZT26ikmPwRERGRqHC1LxERERGJBpM/IiIiEhfBTJsRYmNjIZFItDalUvm/kAQBsbGx8Pb2hp2dHcLCwnDu3DnT3mcpmPwRERGRqEjU5tmMFRgYiPT0dM125swZzb6ZM2dizpw5+Oqrr3D8+HEolUp07NgR9+/fN+M7f4TJHxEREdEzYGNjA6VSqdnc3d0BPOr1mzdvHiZOnIjXX38dDRs2xPLly/Hw4UOsXr3a7HEw+SMiIiJxMeOwr0ql0try8/NLPe3ly5fh7e2NmjVrom/fvrh27RoAICUlBRkZGejUqZOmrkwmQ9u2bXH48GFzvnMATP6IiIhIZEpW+5q6AYCPjw8UCoVmi4+P13vOFi1aYMWKFdi1axeWLFmCjIwMtG7dGnfu3EFGRgYAwNPTU+sYT09PzT5z4q1eiIiISFzMeJ+/tLQ0ODs7a4plMpne6l27dtV8HRQUhFatWqFWrVpYvnw5WrZsCQCQSCRPnELQKTMH9vwRERERlZOzs7PWVlry9yQHBwcEBQXh8uXLmlW/T/byZWZm6vQGmgOTPyIiIhIVcw77lld+fj4uXLgALy8v1KxZE0qlErt379bsLygowP79+9G6dWsT360uDvsSERGRuJTjPn162zBCTEwMunfvjho1aiAzMxPTpk2DSqXC4MGDIZFIEBUVhbi4ONSpUwd16tRBXFwc7O3t0b9/fxMD1cXkj4iIiKiC3bhxA/369cPt27fh7u6Oli1b4ujRo/D19QUAjB07Frm5uRg1ahTu3buHFi1a4JdffoGTk5PZY2HyR0RERKJiiWf7rl27tuz2JBLExsYiNja2/EEZiMkfERERiYsZV/tWRlzwQURERCQi7PkjIiIiUbHEsO/zhMkfERERiYsFVvs+TzjsS0RERCQi7PkjIiIiUeGwLxEREZGYqIVHm6ltVFJM/oiIiEhcOOePiIiIiMSCPX9EREQkKhKYYc6fWSKxDCZ/REREJC58wgcRERERiQV7/oiIiEhUeKsXIiIiIjHhal8iIiIiEgv2/BEREZGoSAQBEhMXbJh6vCUx+SMiIiJxUf93M7WNSorDvkREREQiwp4/IiIiEhUO+xIRERGJichX+zL5IyIiInHhEz6IiIiISCzY80dERESiwid8EFGlY2UtYODoNIR3v4Wq7oW4e8sWezZ6YM3X1SEIEkuHR2aWeOAPeFYv0CnfttIDCyfXtEBEZC6BTe6i96BU1K6vgqt7PqZGN8bRJE/N/o9iz6BD95tax/x1RoHoiJbPOtQXi8iHfZn8EVVCbw7/B936ZmD2uNr4+7I9AoIe4KP4K8i5b40ty70tHR6Z2Ye9GsLK6n9/aHzr5iJ+5V84+JOrBaMic5DbFSPlkhP2bK2GibNO6a2T/Jsb5n3WUPO6sJD/4JFpOOdPD4lEUuYWERGhqbt9+3aEhYXByckJ9vb2CAkJQWJiomb/6dOnIZPJsHXrVq1zbNiwAXK5HGfPngUAxMbGonHjxlp1VCoVJk6ciHr16kEul0OpVKJDhw7YuHEjhFL+4yguLkZ8fDzq1asHOzs7uLi4oGXLlli2bJmmTkREhOa92Nrawt/fHzExMcjJyQEApKamlvrejx49CgBITEzUu18ul2vFk5GRgQ8++AD+/v6QyWTw8fFB9+7d8euvv2rq+Pn5Yd68eTrvRd81oUfqNbmPo7+64HiSCzL/kePQTjec/K0K6jTMsXRoVAGy79ri3m2pZmvRLgs3U2U4c8zJ0qGRiU4cdsfKRXVweJ9nqXUKC61w745Msz1QSZ9hhC8mido8W2XFnj890tPTNV+vW7cOkyZNwsWLFzVldnZ2AIAFCxYgKioK48aNw8KFCyGVSrFlyxaMHDkSZ8+exaxZsxAcHIxPP/0Uw4cPR2hoKFxdXZGZmYmRI0fis88+Q8OGDXXODwBZWVl4+eWXkZ2djWnTpiEkJAQ2NjbYv38/xo4di3bt2qFKlSo6x8XGxuLbb7/FV199hebNm0OlUiE5ORn37t3TqtelSxcsW7YMhYWFOHjwIIYOHYqcnBwsWrRIU2fPnj0IDAzUOs7V9X89Dc7OzlrXBXiUOJdITU1FaGgoqlSpgpkzZ6JRo0YoLCzErl27EBkZib/++qu0j4Ce4lyyE17p9y+q+eXin1Q71KyXg8Bm97F4up+lQ6MKZmOrRnjP29i0VAmAPUBiENTsLlbt3oec+zY4c7IqVnxdB9n3ZJYOq3LjsC89SalUar5WKBSQSCRaZQCQlpaG6OhoREVFIS4uTlMeHR0NqVSK0aNHo0+fPmjRogXGjx+PrVu3IjIyEmvXrsWIESNQp04dxMTElBrDhAkTkJqaikuXLsHb+3/DeAEBAejXr59OD1uJbdu2YdSoUejTp4+mLDg4WKeeTCbTvKf+/ftj37592Lx5s1by5+rqqvO+H6fvujxu1KhRkEgk+P333+Hg4KApDwwMxLvvvlvqcYbKz89Hfn6+5rVKpTK5zcrix2+rwcGpGN/u+gPqYgmsrAUsn1MD+7e7Wzo0qmCtOt6Do3MRdq/nZy0Gyb+54dAeJTLT5fD0zsXb711B3DfJ+HBgKxQVcvCOyoffOeW0fv16FBYW6k3gRowYAUdHR6xZswYAYG1tjeXLl2PLli3o378/du3ahcTERFhbW+ttW61WY+3atRgwYIBW4lfC0dERNjb683alUom9e/fi1q1bRr0fOzs7FBYWGnVMWe7evYudO3ciMjJSK/Eroa/X0ljx8fFQKBSazcfHx+Q2K4u2r9xBu563MPPjAHzQqxFmj62N3kNuosNrmZYOjSpY5zdvIXl/FdzN5NCfGBzc7YXjh9zx91Un/H7QA5NGN0M13xy89LJxv+PpCYKZtkqKyV85Xbp0CQqFAl5eXjr7pFIp/P39cenSJU1Z/fr1ERUVhTVr1iA2NhYBAQGltn379m3cu3cP9erVMzquOXPm4NatW1AqlWjUqBFGjhyJn3/+ucxjfv/9d6xevRrt27fXKm/dujUcHR21tuLiYs3+7Oxsnf2dOnUCAFy5cgWCIBj8HsaNG6fT1uM9qvqMHz8e2dnZmi0tLc2gc70IhoxLxQ+Lq2H/DjekXnLA3i0e2JTohTdH/GPp0KgCeXjno3FoNnauY6+fWN27LUNmuh28a3B+rylKHu9m6lZZcdi3ggiCoDX/7cGDB1i3bh3s7e1x8OBBjB07tsxjAe35c4Zq0KABzp49ixMnTuDQoUM4cOAAunfvjoiICHz33Xeaetu3b4ejoyOKiopQWFiInj17YsGCBVptrVu3DvXr19cqe7y30snJCSdPntTaXzIf0tj3MGbMGK2FNAAwf/58HDhwoNRjZDIZZDJxznuRydU6t3RRF0sgsaq8v4zo6Tr2uYXsO7b4fV9VS4dCFuKkKIC7Zx7u3hbn7z4yDyZ/5RQQEIDs7GzcvHlTZ2i2oKAA165dQ7t27TRlY8aMgVQqxeHDh9GqVSusWLECgwYN0tu2u7s7qlatigsXLpQrNisrK4SEhCAkJAQfffQRvv/+e7z99tuYOHEiatZ8dE+w8PBwLFq0CLa2tvD29oatra1OOz4+Pqhdu3aZ5yltf506dSCRSHDhwgX06tXrqTG7ubnptOXi4vLU48Tq2L6q6PveDWTelOLvy/ao3SAHr797E7+s97B0aFRBJBIBHd+4hT0b3aAu5kKPF4XcrgjePg81r5XeufAPUOG+yhb3s20xYMRV/ParJ+7elsHTOxeDIy9DlWWLI2WsDiYDiHzBB4d9y6l3796wsbHB7NmzdfZ98803yMnJQb9+/QAAu3fvxnfffYfExEQEBwcjLi4OUVFRWquKH2dlZYW33noLq1atws2bN3X25+TkoKioyOBYGzRooDmuhIODA2rXrg1fX1+9iZ+pXFxc0LlzZ3z99dda5y2RlZVl9nOKyaIp/ji00xWRsdfw7c5TGPpJKn5aq8SKeTUsHRpVkCah2fCsVoBffuSQ74ukTgMVFqw5ggVrjgAAhkVfxII1RzBw5BWo1RL41r6PT+f8gW83HcTHn53BP9ftEf1OC+Q+ZN+NSQQAahO3ypv7seevvGrUqIGZM2ciJiYGcrkcb7/9NmxtbbFlyxZMmDAB0dHRaNGiBVQqFYYMGYKYmBi0bPnojuyjR4/Ghg0bMHz4cGzbtk1v+3FxcUhKSkKLFi0wffp0NG/eHLa2tjh48CDi4+Nx/PhxvYsm3njjDYSGhqJ169ZQKpVISUnB+PHjERAQYPQcwjt37iAjI0OrrEqVKpqVxoIg6OwHAA8PD1hZWWHhwoVo3bo1XnrpJUyZMgWNGjVCUVERdu/ejUWLFpW7Z5OA3BxrLJ5eE4un8+kOYnHyUBV09W9h6TDIzM6ccMErzTqXun/S+82fYTTiYY45e5zzJ1IfffQRatWqhVmzZuHLL79EcXExAgMDsWjRIrzzzjsAgKioKCgUCnz22Wea46ysrLBs2TIEBweXOvxbtWpVHD16FJ9//jmmTZuGv//+G1WrVkVQUBC++OILKBQKvTF17twZa9asQXx8PLKzs6FUKtGuXTvExsaWukK4NB06dNApW7NmDfr27Qvg0a1V9C14SU9Ph1KpRM2aNXHy5ElMnz4d0dHRSE9Ph7u7O5o1a6Z1SxkiIiJ6diRCaY+KIKpkVCoVFAoF2jn0g42Et8F44T228pxefFZVq1g6BHoGitQF2JPxLbKzs+Hs7Gz29jV/Jxp/Ahtr0xbNFBXnY++pzyss1orEnj8iIiISFy74ICIiIiKxYM8fERERiYsapj8aW22OQCyDyR8RERGJithX+3LYl4iIiKiCxcfHIyQkBE5OTvDw8ECvXr1w8eJFrToRERGQSCRaW8lt4syJyR8RERGJS8mCD1M3I+zfvx+RkZE4evQodu/ejaKiInTq1EnnQQhdunRBenq6Zvvpp5/M+c4BcNiXiIiIxMaMq31VKpVWcWnPnd+5c6fW62XLlsHDwwMnTpxAmzZttI5XKpWmxfYU7PkjIiIiKicfHx8oFArNFh8fb9Bx2dnZAHSfY5+UlAQPDw8EBARg2LBhyMzMNHvM7PkjIiIicTFjz19aWprWTZ719frpHirg448/xssvv4yGDRtqyrt27Yo+ffrA19cXKSkp+PTTT9GuXTucOHHCoHYNxeSPiIiIxMWMt3pxdnY2+gkf77//Pv78808cOnRIq/ytt97SfN2wYUM0b94cvr6+2LFjB15//XUTA/4fJn9EREQkKpa81csHH3yArVu34sCBA6hevXqZdb28vODr64vLly+X61ylYfJHREREVMEEQcAHH3yATZs2ISkpCTVr1nzqMXfu3EFaWhq8vLzMGgsXfBAREZG4WOBWL5GRkfj++++xevVqODk5ISMjAxkZGcjNzQUAPHjwADExMThy5AhSU1ORlJSE7t27w83NDa+99ppZ3z57/oiIiEhc1AIgMXHBh9q44xctWgQACAsL0ypftmwZIiIiYG1tjTNnzmDFihXIysqCl5cXwsPDsW7dOjg5OZkW6xOY/BERERFVMOEpPYV2dnbYtWvXM4mFyR8RERGJixlv9VIZMfkjIiIikTFD8ofKm/xxwQcRERGRiLDnj4iIiMSFw75EREREIqIWYPKwrZGrfZ8nHPYlIiIiEhH2/BEREZG4COpHm6ltVFJM/oiIiEhcOOePiIiISEQ454+IiIiIxII9f0RERCQuHPYlIiIiEhEBZkj+zBKJRXDYl4iIiEhE2PNHRERE4sJhXyIiIiIRUasBmHifPnXlvc8fh32JiIiIRIQ9f0RERCQuHPYlIiIiEhGRJ38c9iUiIiISEfb8ERERkbiI/PFuTP6IiIhIVARBDUEwbbWuqcdbEpM/IiIiEhdBML3njnP+iIiIiKgyYM8fERERiYtghjl/lbjnj8kfERERiYtaDUhMnLNXief8cdiXiIiISETY80dERETiwmFfIiIiIvEQ1GoIJg77VuZbvXDYl4iIiEhE2PNHRERE4sJhXyIiIiIRUQuARLzJH4d9iYiIiESEPX9EREQkLoIAwNT7/FXenj8mf0RERCQqglqAYOKwr8Dkj4iIiKiSENQwveePt3ohIiIiokqAPX9EREQkKhz2JSIiIhITkQ/7MvmjF0bJf2FFQqGFI6FnQii2dAT0DFmpCywdAj0DRf/9nCu6V60IhSbf47kIlfdvDZM/emHcv38fAHDg4XoLR0JEZpdh6QDoWbp//z4UCoXZ25VKpVAqlTiU8ZNZ2lMqlZBKpWZp61mSCJV50JroMWq1Gjdv3oSTkxMkEomlw3lmVCoVfHx8kJaWBmdnZ0uHQxWIn7V4iPWzFgQB9+/fh7e3N6ysKmZNal5eHgoKzNOTLJVKIZfLzdLWs8SeP3phWFlZoXr16pYOw2KcnZ1F9UdCzPhZi4cYP+uK6PF7nFwur5QJmznxVi9EREREIsLkj4iIiEhEmPwRVXIymQyTJ0+GTCazdChUwfhZiwc/a6pIXPBBREREJCLs+SMiIiISESZ/RERERCLC5I+IiIhIRJj8EREREYkIkz+i/4qIiECvXr10ypOSkiCRSJCVlaWzr27dupBKpfjnn3+06pa1JSYmllkvI6P051ht2LABLVq0gEKhgJOTEwIDAxEdHa3Zn5iYqNWWl5cX3nzzTaSkpGjq+Pn56T3v559/DgBITU0tNbajR49q2ikoKMDMmTMRHBwMe3t7uLm5ITQ0FMuWLUNhYWG5r2lZn8f69eshl8sxc+ZMAEBsbKzeOOvVq6c5JiwsDFFRUVqvJRIJ1q5dq9X2vHnz4OfnV+q1LNnKujlsWe/Lz88P8+bN0ymPi4uDtbW15vqX1C3reygsLKzMeo+39aRr166hX79+8Pb2hlwuR/Xq1dGzZ09cunRJU+fxtpycnNC8eXNs3LhRs9/Q666vzsiRI7Xi2bdvH7p16wZXV1fY29ujQYMGiI6O1vmZMvSaPu3nLyIiQlN3+/btCAsLg5OTE+zt7RESEoLExETN/tOnT0Mmk2Hr1q1a59iwYQPkcjnOnj2ruR6NGzfWqqNSqTBx4kTUq1cPcrkcSqUSHTp0wMaNG0t9bm1xcTHi4+NRr1492NnZwcXFBS1btsSyZcs0dSIiIjTvxdbWFv7+/oiJiUFOTg4Aw35+Df3ezsjIwAcffAB/f3/IZDL4+Pige/fu+PXXX8v8DEq7JvT84BM+iMrp0KFDyMvLQ58+fZCYmIiJEyeidevWSE9P19T58MMPoVKptH55KxQKHDt2DABw8eJFnbv3e3h46D3fnj170LdvX8TFxaFHjx6QSCQ4f/681i9i4NETAS5evAhBEPDXX39hxIgR6NGjB06dOgVra2sAwJQpUzBs2DCt45ycnHTOFxgYqFXm6uoK4FHi17lzZ5w+fRpTp05FaGgonJ2dcfToUcyaNQtNmjQx+y/+7777DpGRkfj6668xdOhQTXlgYCD27NmjVdfGpuxfbXK5HP/5z3/Qu3dv2Nrallqv5Fo+ztyPDly2bBnGjh2LpUuX4pNPPgEAHD9+HMXFxQCAw4cPo3fv3lrfK48/S9SQz7JEQUEBOnbsiHr16mHjxo3w8vLCjRs38NNPPyE7O1snri5duiArKwtffPEF+vTpg0OHDqFVq1YADLvuw4YNw5QpU7TK7O3tNV8vXrwYo0aNwuDBg7Fhwwb4+fnh+vXrWLFiBWbPno05c+aUffH0ePznb926dZg0aZLWZ2hnZwcAWLBgAaKiojBu3DgsXLgQUqkUW7ZswciRI3H27FnMmjULwcHB+PTTTzF8+HCEhobC1dUVmZmZGDlyJD777DM0bNhQbwxZWVl4+eWXkZ2djWnTpiEkJAQ2NjbYv38/xo4di3bt2qFKlSo6x8XGxuLbb7/FV199hebNm0OlUiE5ORn37t3TqtelSxfNP1kHDx7E0KFDkZOTg0WLFmnqlPXzCzz9ezs1NRWhoaGoUqUKZs6ciUaNGqGwsBC7du1CZGQk/vrrr9I+AqoEmPwRlVNCQgL69++Ptm3bIjIyEhMmTNA8NLyEnZ0d8vPztcoe5+HhofePgD7bt2/Hyy+/jDFjxmjKAgICdHrHJBKJ5nxeXl6YPHkyBg4ciCtXrqBu3boAHiUHpcVUwtXVtdQ68+bNw4EDB5CcnIwmTZpoyv39/dGnTx+zPTezxMyZMzFp0iSsXr0avXv31tpnY2Pz1PfypH79+mHbtm1YsmQJRo0aVWq9x69lRdi/fz9yc3MxZcoUrFixAgcOHECbNm3g7u6uqePi4gKg9O8VQz7LEufPn8e1a9ewd+9e+Pr6AgB8fX0RGhqqU7dKlSpQKpVQKpX45ptvsHbtWmzdulWT/Bly3e3t7Uutc+PGDYwePRqjR4/G3LlzNeV+fn5o06ZNmb3CZXn8fAqFQu9nmJaWhujoaERFRSEuLk5THh0dDalUitGjR6NPnz5o0aIFxo8fj61btyIyMhJr167FiBEjUKdOHcTExJQaw4QJE5CamopLly7B29tbUx4QEIB+/fqV2nu8bds2jBo1Cn369NGUBQcH69STyWSa99S/f3/s27cPmzdv1kr+yvr5BZ7+vT1q1ChIJBL8/vvvcHBw0JQHBgbi3XffLfU4qhw47EtUDvfv38ePP/6IgQMHomPHjsjJyUFSUlKFnlOpVOLcuXOaoSZDlfR0lAzFmsOqVavQoUMHrcSvhK2trdYfC1N98sknmDp1KrZv366T+JWXs7MzJkyYgClTpmiGyywhISEB/fr1g62tLfr164eEhIQKPZ+7uzusrKywfv16Tc+iIWxtbWFjY2PW76Eff/wRBQUFGDt2rN79hv5TVB7r169HYWGh3gRuxIgRcHR0xJo1awAA1tbWWL58ObZs2YL+/ftj165dSExM1PSiP0mtVmPt2rUYMGCAVuJXwtHRsdSeaaVSib179+LWrVtGvR87OzuzfjZ3797Fzp07ERkZqfdnuSI/G3o2mPwRPWb79u1wdHTU2rp27apTb+3atahTpw4CAwNhbW2Nvn37lusPd/Xq1bXOVdIzp88HH3yAkJAQBAUFwc/PD3379sXSpUuRn59f6jE3btzAF198gerVqyMgIEBTPm7cOJ33+WTy2rp1a506JQnD5cuXteZ3lcXQa6rPzz//jBkzZmDLli3o0KGD3jpnzpzRaf/xYeHSjBo1CnK5vMyhxezsbJ22O3Xq9NS2n/xcHR0dcf36da06KpUKGzZswMCBAwEAAwcOxPr166FSqZ7a/uMM+SxLVKtWDfPnz8ekSZNQtWpVtGvXDlOnTsW1a9dKbT8/Px/Tpk2DSqVC+/btNeWGXPeFCxfq1Fm+fDmAR99Dzs7O8PLyMuh9GnJNDXXp0iUoFAq955ZKpfD399eaA1m/fn1ERUVhzZo1iI2N1fpZetLt27dx7949g38+HjdnzhzcunULSqUSjRo1wsiRI/Hzzz+Xeczvv/+O1atXa302QNk/v0DZ39tXrlyBIAgGvwd934OP96jS84fDvkSPCQ8P1xo6AYBjx45p/kCXSEhI0CobOHCgZqjKmP+KDx48qDU/q6y5ag4ODtixYweuXr2Kffv24ejRo4iOjsaXX36JI0eOaOZSlfxSFwQBDx8+RNOmTbFx40ateWJjxozRmvgOPEoMHrdu3TrUr19fq6ykt0MQBIPnvhl6TfVp1KgRbt++jUmTJiEkJETvXLa6devqTMgvbc7b42QyGaZMmYL3338f7733nt46Tk5OOHnypFZZSU9qWZ78XAFoFmmUWL16Nfz9/TXDeo0bN4a/vz/Wrl2L4cOHP/UcJQz5LB8XGRmJQYMGYd++fTh27Bh+/PFHxMXFYevWrejYsaOmXr9+/WBtbY3c3FwoFArMmjVLK2k35LoPGDAAEydO1CormdNqzPcQYNg1NZcnY3vw4AHWrVsHe3t7HDx4sNTeypJjgfLNDW3QoAHOnj2LEydO4NChQzhw4AC6d++OiIgIfPfdd5p6Jf9QFRUVobCwED179sSCBQu02irr5xco+3vb2Peg73tw/vz5OHDggEHH07PH5I/oMQ4ODqhdu7ZW2Y0bN7Renz9/HseOHcPx48cxbtw4TXlxcTHWrFlTaiKhT82aNY0eQqlVqxZq1aqFoUOHYuLEiQgICMC6devwzjvvAPjfL3UrKyt4enrqHbZxc3PTeZ9P8vHxKbVOQEAALly4YFC8hlzT0lSrVg0bNmxAeHg4unTpgp07d+okAFKp9KnvpTQDBw7ErFmzMG3aNK2VviWsrKzK1ba+z/XJxH7p0qU4d+6cVrlarUZCQoJRyZ8hn+WTnJyc0KNHD/To0QPTpk1D586dMW3aNK3kb+7cuejQoQOcnZ31LkIy5LorFIoyv4eys7ORnp5uUO+fIdfUUCXnvnnzps7QbEFBAa5du4Z27dppysaMGQOpVIrDhw+jVatWWLFiBQYNGqS3bXd3d1StWtXgn48nWVlZISQkBCEhIfjoo4/w/fff4+2338bEiRNRs2ZNAP/7h8rW1hbe3t56Fy2V9fNbcp7S9tepUwcSiQQXLlzQu1r/Sfq+B0vmqtLzicO+REZKSEhAmzZtcPr0aZw6dUqzjR07tsLnbD3Jz88P9vb2WvPWSn6p+/v7m3Xu3eP69++PPXv24I8//tDZV1RUZNZ5dDVq1MD+/fuRmZmJTp06GT0sWhYrKyvExcVh0aJFSE1NNVu7T3PmzBkkJycjKSlJ63vowIEDOH78uNHzOk1RcouWJz8zpVKJ2rVrl7r63FRvvPEGpFKp5rY9Tyrvgg9D9O7dGzY2Npg9e7bOvm+++QY5OTno168fAGD37t347rvvkJiYiODgYMTFxSEqKkprVfHjrKys8NZbb2HVqlW4efOmzv6cnBwUFRUZHGuDBg00x5Uo+YfK19e3zNXq5eXi4oLOnTvj66+/1vuzXJGfDT0b7PkjMkJhYSFWrlyJKVOm6NzmYejQoZg5cyZOnz6td4WePpmZmcjLy9Mqc3V11fsLPTY2Fg8fPkS3bt3g6+uLrKwszJ8/H4WFhVo9Noa4f/++zv0E7e3ttW47c+fOHZ06VapUgVwuR1RUFHbs2IH27dtj6tSpePnll+Hk5ITk5GTMmDEDCQkJZr3VS/Xq1ZGUlITw8HB06tQJu3btgkKhAPAo2XwyTolEAk9PT4PafvXVV9GiRQssXrxY5xhBEPTed9HDwwNWVuX/3zkhIQEvvfQS2rRpo7OvVatWSEhI0FoBWxZDPssSp06dwuTJk/H222+jQYMGkEql2L9/P5YuXarVi20IQ677w4cPderIZDJUrVoVPj4+mDt3Lt5//32oVCoMGjQIfn5+uHHjBlasWAFHR0e9yZk51KhRAzNnzkRMTAzkcjnefvtt2NraYsuWLZgwYQKio6PRokULqFQqDBkyBDExMWjZsiUAYPTo0diwYQOGDx+Obdu26W0/Li4OSUlJaNGiBaZPn47mzZvD1tYWBw8eRHx8PI4fP663x/+NN95AaGgoWrduDaVSiZSUFIwfPx4BAQFGzyEs6+cXePr39sKFC9G6dWu89NJLmDJlCho1aoSioiLs3r0bixYtKnfPJj0nBCISBEEQBg8eLPTs2VOnfN++fQIA4d69e8L69esFKysrISMjQ28bQUFBwgcffGBwm/q2I0eO6G177969Qu/evQUfHx9BKpUKnp6eQpcuXYSDBw9q6ixbtkxQKBRlvk9fX1+95x0xYoQgCIKQkpJSamxr1qzRtJOXlyfEx8cLQUFBglwuF1xcXITQ0FAhMTFRKCwsNPialkbfsTdv3hTq1q0rhISECPfu3RMmT56sN06ZTKY5pm3btsKHH35Y6mtBEITDhw8LAARfX1+ta1nadUhPT9cbc1nvy9fXV5g7d66Qn58vuLq6CjNnztTbxuzZswU3NzchPz/foDbL+iyfdOvWLWH06NFCw4YNBUdHR8HJyUkICgoSZs2aJRQXF2vqARA2bdqktw1BEAy+7vrqdO7cWaut3bt3C507dxaqVq0qyOVyoV69ekJMTIxw8+ZNg69paZ7287Blyxbh//7v/wQHBwdBLpcLzZo1E5YuXarZ/8477wgNGzbUfBYlLl++LNjb2wvLly/XXI/g4GCtOllZWcInn3wi1KlTR/Pz2qFDB2HTpk2CWq3WG8+3334rhIeHC+7u7oJUKhVq1KghRERECKmpqZo6pf1MlTDk59fQ7+2bN28KkZGRgq+vryCVSoVq1aoJPXr0EPbt26epU9pnoO+a0PNDIgil3GqciIiIiF44nPNHREREJCJM/oiIiIhEhMkfERERkYgw+SMiIiISESZ/RERERCLC5I+IiIhIRJj8EREREYkIkz8iIiIiEWHyR0RkRrGxsVqPtouIiECvXr2eeRypqamQSCQ4depUqXX8/Pwwb948g9tMTEzU+1gyY0kkEmzevNnkdoiofJj8EdELLyIiAhKJBBKJBLa2tvD390dMTIzeh9ab25dffonExESD6hqSsBERmcrG0gEQET0LXbp0wbJly1BYWIiDBw9i6NChyMnJwaJFi3TqFhYWwtbW1iznVSgUZmmHiMhc2PNHRKIgk8mgVCrh4+OD/v37Y8CAAZqhx5Kh2qVLl8Lf3x8ymQyCICA7OxvDhw+Hh4cHnJ2d0a5dO5w+fVqr3c8//xyenp5wcnLCkCFDkJeXp7X/yWFftVqNGTNmoHbt2pDJZKhRowamT58OAKhZsyYAoEmTJpBIJAgLC9Mct2zZMtSvXx9yuRz16tXDwoULtc7z+++/o0mTJpDL5WjevDn++OMPo6/RnDlzEBQUBAcHB/j4+GDUqFF48OCBTr3NmzcjICAAcrkcHTt2RFpamtb+bdu2oVmzZpDL5fD398dnn32GoqIio+MhoorB5I+IRMnOzg6FhYWa11euXMEPP/yADRs2aIZdX3nlFWRkZOCnn37CiRMn0LRpU7Rv3x53794FAPzwww+YPHkypk+fjuTkZHh5eekkZU8aP348ZsyYgU8//RTnz5/H6tWr4enpCeBRAgcAe/bsQXp6OjZu3AgAWLJkCSZOnIjp06fjwoULiIuLw6efforly5cDAHJycvDqq6+ibt26OHHiBGJjYxETE2P0NbGyssL8+fNx9uxZLF++HHv37sXYsWO16jx8+BDTp0/H8uXL8dtvv0GlUqFv376a/bt27cLAgQMxevRonD9/HosXL0ZiYqImwSWi54BARPSCGzx4sNCzZ0/N62PHjgmurq7Cm2++KQiCIEyePFmwtbUVMjMzNXV+/fVXwdnZWcjLy9Nqq1atWsLixYsFQRCEVq1aCSNHjtTa36JFCyE4OFjvuVUqlSCTyYQlS5bojTMlJUUAIPzxxx9a5T4+PsLq1au1yqZOnSq0atVKEARBWLx4seDi4iLk5ORo9i9atEhvW4/z9fUV5s6dW+r+H374QXB1ddW8XrZsmQBAOHr0qKbswoULAgDh2LFjgiAIwv/93/8JcXFxWu2sXLlS8PLy0rwGIGzatKnU8xJRxeKcPyIShe3bt8PR0RFFRUUoLCxEz549sWDBAs1+X19fuLu7a16fOHECDx48gKurq1Y7ubm5uHr1KgDgwoULGDlypNb+Vq1aYd++fXpjuHDhAvLz89G+fXuD47516xbS0tIwZMgQDBs2TFNeVFSkmU944cIFBAcHw97eXisOY+3btw9xcXE4f/48VCoVioqKkJeXh5ycHDg4OAAAbGxs0Lx5c80x9erVQ5UqVXDhwgW89NJLOHHiBI4fP67V01dcXIy8vDw8fPhQK0Yisgwmf0QkCuHh4Vi0aBFsbW3h7e2ts6CjJLkpoVar4eXlhaSkJJ22ynu7Ezs7O6OPUavVAB4N/bZo0UJrn7W1NQBAEIRyxfO4v//+G926dcPIkSMxdepUuLi44NChQxgyZIjW8Djw6FYtTyopU6vV+Oyzz/D666/r1JHL5SbHSUSmY/JHRKLg4OCA2rVrG1y/adOmyMjIgI2NDfz8/PTWqV+/Po4ePYpBgwZpyo4ePVpqm3Xq1IGdnR1+/fVXDB06VGe/VCoF8KinrISnpyeqVauGa9euYcCAAXrbbdCgAVauXInc3FxNgllWHPokJyejqKgIs2fPhpXVo+ngP/zwg069oqIiJCcn46WXXgIAXLx4EVlZWahXrx6AR9ft4sWLRl1rInq2mPwREenRoUMHtGrVCr169cKMGTNQt25d3Lx5Ez/99BN69eqF5s2b48MPP8TgwYPRvHlzvPzyy1i1ahXOnTsHf39/vW3K5XKMGzcOY8eOhVQqRWhoKG7duoVz585hyJAh8PDwgJ2dHXbu3Inq1atDLpdDoVAgNjYWo0ePhrOzM7p27Yr8/HwkJyfj3r17+Pjjj9G/f39MnDgRQ4YMwX/+8x+kpqZi1qxZRr3fWrVqoaioCAsWLED37t3x22+/4ZtvvtGpZ2triw8++ADz58+Hra0t3n//fbRs2VKTDE6aNAmvvvoqfHx80KdPH1hZWeHPP//EmTNnMG3aNOM/CCIyO672JSLSQyKR4KeffkKbNm3w7rvvIiAgAH379kVqaqpmde5bb72FSZMmYdy4cWjWrBn+/vtvvPfee2W2++mnnyI6OhqTJk1C/fr18dZbbyEzMxPAo/l08+fPx+LFi+Ht7Y2ePXsCAIYOHYrvvvsOiYmJCAoKQtu2bZGYmKi5NYyjoyO2bduG8+fPo0mTJpg4cSJmzJhh1Ptt3Lgx5syZgxkzZqBhw4ZYtWoV4uPjderZ29tj3Lhx6N+/P1q1agU7OzusXbtWs79z587Yvn07du/ejZCQELRs2RJz5syBr6+vUfEQUcWRCOaYLEJERERElQJ7/oiIiIhEhMkfERERkYgw+SMiIiISESZ/RERERCLC5I+IiIhIRJj8EREREYkIkz8iIiIiEWHyR0RERCQiTP6IiIiIRITJHxEREZGIMPkjIiIiEpH/B+x7UJza3885AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_6_t_1_trn_100>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_6__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_6__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:52<00:00,  1.69it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.676829268292683,\n",
      "                 'precision': 0.6032608695652174,\n",
      "                 'recall': 0.7708333333333334,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8083491461100569,\n",
      "                       'precision': 0.7859778597785978,\n",
      "                       'recall': 0.83203125,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.4962406015037594,\n",
      "                  'precision': 0.8461538461538461,\n",
      "                  'recall': 0.35106382978723405,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.7226720647773279,\n",
      " 'macro avg': {'f1-score': 0.6604730053021665,\n",
      "               'precision': 0.7451308584992206,\n",
      "               'recall': 0.6513094710401891,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.7106222926714054,\n",
      "                  'precision': 0.7441667183384088,\n",
      "                  'recall': 0.7226720647773279,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_6_t_1_trn_100>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>█▄▇▆▆█▄▅▁▃▁▂▃▂▂▄</td></tr><tr><td>train_mean_token_accuracy</td><td>▁▅▃▃▄▅▄▄█▆▆▅▅▇▆▆</td></tr><tr><td>valid_loss</td><td>▂▁▂▂▄▄▃▃▂▄▃█▂▆▁▁</td></tr><tr><td>valid_mean_token_accuracy</td><td>▆▆▅█▁▃▁▅▆▃▇▂▇▅▆▇</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.72267</td></tr><tr><td>f1</td><td>0.72267</td></tr><tr><td>precision</td><td>0.72267</td></tr><tr><td>recall</td><td>0.72267</td></tr><tr><td>train_loss</td><td>0.04792</td></tr><tr><td>train_mean_token_accuracy</td><td>0.98551</td></tr><tr><td>valid_loss</td><td>0.06901</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.9726</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_research_assistants</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/ntp9oned</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
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    "# Mark the end of the run\n",
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